If this is a task you're going to do repeatedly, and your polygon list is relatively static, then you should consider redefining the problem. I suspect that the loop and indexing operations my be consuming a considerable amount of your time. So maybe you can change the problem to unroll the loops.

Yes, the polys are relatively static, although the points are not so. Nevertheless, I do indeed unroll both polys and points, and move them into the SQLite db. That way I avoid going back to the files repeatedly.

You are correct about "loop and indexing operations my be consuming a considerable amount of your time". Below is the output from my latest run of 5000 polys using Devel::Profiler. More than 220k points were evaluated and updated, and my point-in-poly test was performed 220k times.

What I really have to figure out is to reduce those tests. I have an idea which I have to now figure out how to implement. Basically, it is like so --

Once I get a bunch of potential points within the rect of a poly, I should find the 4 outermost points in each dimension. Once I determine those four definitively, all points within those 4 would be also within that poly, and I wouldn't have to do the checks for them.

Once I get a bunch of potential points within the rect of a poly, I should find the 4 outermost points in each dimension. Once I determine those four definitively, all points within those 4 would be also within that poly, and I wouldn't have to do the checks for them.

If I understand you correctly, then given the polygon below and the four points marked as dots ("."), wouldn't that method determine that the point marked with an "x" is inside the polygon?

If all of your polygons were guananteed to have internal angles > 90 degrees that might work, but you only said that they were "irregular". I had a similar idea yesterday (except mine was finding the maximum internal bounding rectangle for the polygon rather than using the query points themselves), and discarded it for this reason. :-)

That's why I made my original suggestion. There are two ways that breaking the polygons into simple shapes will help you:

With known shapes, your _pointIsInPolygon function is simpler, so you can use quick exits and/or the compiler can optimize it, and

you can increase your density of polygon vs. bounding boxes.

In your original _pointIsInPolygon, you use variable indices into your vertex arrays, as well as adding a loop with it's inherent termination condition. By breaking your polygons into triangles or quadrilaterals, you can use fixed indices and omit the loop. That may significantly increase the subroutines speed. Also, your algorithm requires that you examine every edge in the polygon. By enforcing an order to the edges, you may be able to return early from the routine by taking advantage of that knowledge.

Regarding the 'density of polygon vs. bounding boxes': A rectangle aligned with your X and Y axes has a 100% chance of containing a point returned by your SELECT statement. An arbitrary triangle has a 50% chance of containing a point in the bounding rectangle. So breaking your polygons into triangles and (aligned) rectangles will have a worst case coverage of 50%. And a higher coverage density directly correlates to a lower false-positive rate. (I.e., with a higher coverage, you'll need fewer tests because your SELECT will hit fewer bounding boxes.) With arbitrary polygons, you can have nearly *any* hit rate:

While I still think my original suggestion would be interesting, you state:

Yes, the polys are relatively static, although the points are not so. Nevertheless, I do indeed unroll both polys and points, and move them into the SQLite db. That way I avoid going back to the files repeatedly.

By this, I'm assuming you mean that the points are in the same rough relative position in the polygons, meaning that (1) the points are connected in the same order, and (2) the edges will never cross. For example, the shape on the left could morph into the shape in the center, but not the one on the right:

In that case, breaking the polygons into X-aligned trapezoids (my original suggestion) may be a bit too restrictive. Perhaps using arbitrary quadrilaterals (or triangles) would give you (a) enough coverage density (i.e.just breaking the shape into quadrilaterals instead of trapezoids may simplify things enough. So you'd increase your coverage, and by breaking the polygons into triangles, you could still simplify your _pointIsInPolygon subroutine to eliminate the loops and variable indices.